Latest Contributions by Elijah

‎12-06-2021

I need to copy a map legend from ArcGIS Pro 2.9, of course from the layout view. I need to copy and paste it on MS word, but it's not working even though the functionality seems to be there. I also tried using the 'capture to clipboard' but this captures everything in the lay out view. Please, can you point me to a solution? I dont want to snipe it since the quality of the graphs fades. Thank you.

‎11-26-2021

I want to assess the impact of sampling density on the accuracy of interpolation using a particular interpolation method. The idea is to sample at different resolutions (grid-based), for example, 1 sample per 2kmx2km grid, 3kmx3km grid , 1 sample per 5kmx5km grid, etc. These sample points would be extracted from existing interpolated grid at the various sampling densities. The question is this: Given the fact that the supposed observed data (samples extracted from existing interpolated grid) are indeed interpolated, what possible impact may it have on the result of this study. Note that, the intent is to assess the interpolation accuracy using Root Mean Square Error (RMSE), Mean Error (ME), etc. Thanks,

‎11-17-2021

Dear Eric, Again, I quite appreciate your answers. I need a few more insight if possible. You have well-dealt with possible number of samples implied by 'small'. However, knowing that the number of samples is not linearly related to sampling density, is there anything specifically advantageous about using EBKRP, for instance, in low sampling density scenarios? Generally, interpolation accuracies are affected by the sampling density and distribution. I will like to know if, perhaps by means of estimating the semi-variogram through the process of sub-setting and repeated simulations, EBKRP can achieve better result than other kriging models, in low sampling density setting. The whole idea is to say or not, that, in a data-scarce (sparse) / low density sampling situation setting (not necessarily small number of samples), EBKRP will perform better than other kriging models, based on its intrinsic characteristics. Thanks in advance.

‎11-16-2021

Hi Eric, Your answer is very much appreciated. Many thanks.

‎11-13-2021

Hi Dan, Thanks for your reply. Yes, I mean the last 2 bullet points contained in the 'Advantages of Empirical Bayesian Kriging' as found here https://pro.arcgis.com/en/pro-app/latest/help/analysis/geostatistical-analyst/what-is-empirical-bayesian-kriging-.htm . I am particularly interested in understanding what is referred to 'as small data set' and how EBK is more accurate than other kriging options when using 'small data sets'. How small is small, for example? Perhaps, looking at sampling density, etc?. It will be good to see (case) studies proving EBK's superior performance in a 'small data set' setting. I am just curious. I can't yet see such articles or demonstrations yet. Thanks a lot

‎11-12-2021

I am trying to understand these descriptions below of EBK/EBKRP. In particular I need to understand what is meant by 'small data sets' here. Does it give any idea of how well EBK performs under different sampling densities, etc? ........EBKRP manages to achieve better accuracy than other kriging techniques both for small datasets and even when data is locally moderately non-stationary (Krivoruchko, 2012). ........EBKRP have shown that the prediction intervals obtained by EBK have good coverage probabilities when the data variation is changing rapidly and dissimilarly in different parts of the data extent (Gribov and Krivoruchko 2020).

‎10-31-2021

I used EBK Regression Prediction @Geostatistical Analyst Pro 2.8 to predict air pollution parameter using 5 explanatory rasters. One of the rasters was an Euclidean distance raster based on distance from a road feature. All the samples were taken along the road feature. The prediction result completely followed the road feature, which is fine, in my view, because I was estimating CO concentration along the road. This of course resulted to a high standard error in areas away from the road. Again, this is expected since sampling wasn't done outside the road feature/line. But I wanted to find out how the road raster was weighted (prioritized) such that it had the highest weight (presumably) to determine the configuration of the prediction map? I have attached a sample map for your view.

‎09-10-2021

This is an important issue. It is widely regarded that there is no significant difference between the 2 sensors in what the NDVIs should be given your context. However, research result in this area is not very consistent. Having said that, some factor that may complicate matters are the projection and preprocessing. You may need to be sure that these preprocessing follows same procedure and projections are accurate. I have dropped below comments from this source which I think may help your decision in this regard. https://www.researchgate.net/post/Is-it-possible-to-have-NDVI-equivalence-between-images-from-Landsat-5-and-Landsat-8

‎09-08-2021

This may look a bit familiar but I am not sure about a few things. I want to investigate the possible relationship between crime incidents and some environmental variables such as the: Distance to roads, distance to residential areas, land use and population density. Note that the Dependent variable is a count/discrete data (crime incident points) and all the explanatory variables are rasters and continuous variables (land use is categorical, which I intend to include using the dummy variable route). 1. I intend to convert all the rasters including the land use raster to points. Is this really okay? 2. Can I use the raw crime data (without aggregating to a polygon such as fishnet, hexagon, etc) in Geographically Weighted Regression (GWR) to assess the relationship as explained above. Or must I aggregate into some kind of polygon for it to work? 3. I was also looking at Forest-based classification... what do you think? Kindly offer your thoughts, please.

‎09-07-2021

I think Raster Domain 3D tool can do it. It outputs a polygon/line geometry of the input raster.

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My Ideas

Latest Contributions by Elijah

How can I copy and paste legend in ArcGIS Pro 2.9?

What could be wrong with interpolating values extracted from an interpolated surface (grid)

Re: Still on EBK/EBK Regression Prediction

Re: Still on EBK/EBK Regression Prediction

Re: Still on EBK/EBK Regression Prediction

Still on EBK/EBK Regression Prediction

How does EBKRP handle each explanatory raster layer in terms of influence in determining the values at an unsampled location?

Re: Comparing NDVI values across Landsat 5 and 8 sensors

How to handle discrete (count) data as dependent variable in Geographically Weighted Regression?

Re: How to Get Polygon of Raster Extent in ArcGIS Pro